U.S. patent application number 17/400436 was filed with the patent office on 2021-12-02 for automatic image inpainting.
The applicant listed for this patent is Snap Inc.. Invention is credited to Kun Duan, Yunchao Gong, Nan Hu.
Application Number | 20210374905 17/400436 |
Document ID | / |
Family ID | 1000005769848 |
Filed Date | 2021-12-02 |
United States Patent
Application |
20210374905 |
Kind Code |
A1 |
Duan; Kun ; et al. |
December 2, 2021 |
AUTOMATIC IMAGE INPAINTING
Abstract
The technical problem of removing an object depicted in a
selected region of an image to create a natural-looking edited
image is addressed by providing systems, methods, and
computer-readable storage media to perform automatic image
inpainting. The method includes replacing the selected region using
a color mask. A color mask can be generated using a mean color of
pixels from a portion of the image that is distinct from and
outside of the selected region.
Inventors: |
Duan; Kun; (Los Angeles,
CA) ; Gong; Yunchao; (Playa Vista, CA) ; Hu;
Nan; (San Jose, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Snap Inc. |
Santa Monica |
CA |
US |
|
|
Family ID: |
1000005769848 |
Appl. No.: |
17/400436 |
Filed: |
August 12, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16829720 |
Mar 25, 2020 |
11107185 |
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17400436 |
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16387110 |
Apr 17, 2019 |
10636119 |
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16829720 |
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16122639 |
Sep 5, 2018 |
10304162 |
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16387110 |
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15448216 |
Mar 2, 2017 |
10127631 |
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16122639 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 5/005 20130101;
G06F 3/04842 20130101; G06T 11/60 20130101; G06F 3/04845 20130101;
G06T 3/40 20130101; G06T 2200/24 20130101 |
International
Class: |
G06T 3/40 20060101
G06T003/40; G06T 11/60 20060101 G06T011/60; G06T 5/00 20060101
G06T005/00; G06F 3/0484 20060101 G06F003/0484 |
Claims
1. A method comprising: identifying a selected region of an image,
the selected region including a depicted object; generating a color
mask using a mean color of pixels located in a local region, the
local region being a portion of the image and being outside of the
selected region; and removing the depicted object from the image by
replacing the selected region with the color mask, the replacing of
the selected region with the color mask yielding a modified image
without the depicted object.
2. The method of claim 1, wherein the operations further comprise:
identifying one or more strong edges in the modified image;
generating an edge map based on the identified one or more strong
edges; generating a binary mask for strong edge pixels by dilating
the edge map; applying blur techniques to the modified image to
yield a blurred image; and blending the modified image with the
blurred image using the binary mask.
3. The method of claim 2, wherein the identifying of the strong
edges includes applying edge-preserving filtering to the modified
image to blur insignificant edges in the modified image.
4. The method of claim 1, wherein the blur techniques include
Gaussian blur.
5. The method of claim 1, wherein the blending of the modified
image with the blurred image includes applying Laplacian blending
to the modified image and the blurred image with the binary
mask.
6. The method of claim 1, wherein the operations comprise
determining the local region based on a size of the selected
region.
7. The method of claim 1, wherein the determining the local region
includes dynamically computing dimensions of the local region based
on dimensions of the selected region.
8. The method of claim 1, wherein the local region surrounds the
selected region.
9. The method of claim 1, wherein the operations further comprise
causing presentation of the modified image without the depicted
object on a display device.
10. The method of claim 1, wherein the selected region is
identified based on an input from a user.
11. A system comprising: a processor; and memory coupled to the
processor and storing instructions that, when executed by the
processor, cause the system to perform operations comprising:
identifying a selected region of an image, the selected region
including a depicted object; generating a color mask using a mean
color of pixels located in a local region, the local region being a
portion of the image and being outside of the selected region; and
removing the depicted object from the image by replacing the
selected region with the color mask, the replacing of the selected
region with the color mask yielding a modified image without the
depicted object.
12. The system of claim 11, wherein the operations further
comprise: identifying one or more strong edges in the modified
image; generating an edge map based on the identified one or more
strong edges; generating a binary mask for strong edge pixels by
dilating the edge map; applying blur techniques to the modified
image to yield a blurred image; and blending the modified image
with the blurred image using the binary mask.
13. The system of claim 12, wherein the identifying of the strong
edges includes applying edge-preserving filtering to the modified
image to blur insignificant edges in the modified image.
14. The system of claim 11, wherein the blur techniques include
Gaussian blur.
15. The system of claim 11, wherein the blending of the modified
image with the blurred image includes applying Laplacian blending
to the modified image and the blurred image with the binary
mask.
16. The system of claim 11, wherein the operations comprise
determining the local region based on a size of the selected
region.
17. The system of claim 11, wherein the determining the local
region includes dynamically computing dimensions of the local
region based on dimensions of the selected region.
18. The system of claim 11, wherein the local region surrounds the
selected region.
19. The system of claim 11, wherein the operations further comprise
causing presentation of the modified image without the depicted
object on a display device.
20. A machine-readable non-transitory storage medium having
instruction data executable by a machine to cause the machine to
perform operations comprising: identifying a selected region of an
image, the selected region including a depicted object; generating
a color mask using a mean color of pixels located in a local
region, the local region being a portion of the image and being
outside of the selected region; and removing the depicted object
from the image by replacing the selected region with the color
mask, the replacing of the selected region with the color mask
yielding a modified image without the depicted object.
Description
PRIORITY CLAIMS
[0001] This application is a continuation of and claims the benefit
of priority of U.S. patent application Ser. No. 16/829,720, filed
on Mar. 25, 2020, which is a continuation of and claims the benefit
of priority of U.S. patent application Ser. No. 16/387,110, filed
on Apr. 17, 2019, which is a continuation of and claims the benefit
of priority of U.S. patent application Ser. No. 16/122,639, filed
on Sep. 5, 2018, which is a continuation of and claims the benefit
of priority of U.S. patent application Ser. No. 15/448,216, filed
on Mar. 2, 2017, each of which is hereby incorporated by reference
herein in their entireties.
TECHNICAL FIELD
[0002] The present disclosure generally relates to the technical
field of special-purpose machines for performing image inpainting,
including computerized variants of such special-purpose machines
and improvements to such variants, and to the technologies by which
such special-purpose machines become improved compared to other
special-purpose machines that perform image inpainting. In
particular, the present disclosure addresses systems and methods
for automatic image inpainting using local patch statistics.
BACKGROUND
[0003] As the popularity of social networking grows, the number of
digital images generated and shared using social networks grows as
well. Prior to sharing such digital images on social networks,
users may wish to remove certain objects depicted in the images or
other undesirable elements of the images. Among other things,
embodiments of the present disclosure help users perform edits to
digital images, such as removing certain regions of an image and
filling these regions with other portions of the image to create a
natural-looking edited image.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] In the drawings, which are not necessarily drawn to scale,
like numerals may describe similar components in different views.
Like numerals having different letter suffixes may represent
different instances of similar components. Some embodiments are
illustrated by way of example, and not limitation, in the figures
of the accompanying drawings.
[0005] FIG. 1 is a block diagram showing an example messaging
system for exchanging data (e.g., messages and associated content)
over a network, according to some embodiments.
[0006] FIG. 2 is block diagram illustrating further details
regarding the messaging system, according to some embodiments.
[0007] FIG. 3 is a schematic diagram illustrating data which may be
stored in a database of the messaging system, according to some
embodiments.
[0008] FIG. 4 is a block diagram illustrating functional components
of an image processing system that forms part of the messaging
system, according to some example embodiments.
[0009] FIGS. 5-8 are flow charts illustrating operations of the
image processing system in performing an example method for digital
image editing, according to some embodiments.
[0010] FIGS. 9A and 9B are interface diagrams illustrating aspects
of user interfaces provided by the messaging system, according to
some embodiments.
[0011] FIG. 10 is a block diagram illustrating a representative
software architecture, which may be used in conjunction with
various hardware architectures herein described.
[0012] FIG. 11 is a block diagram illustrating components of a
machine, according to some exemplary embodiments, able to read
instructions from a machine-readable medium (e.g., a
machine-readable storage medium) and perform any one or more of the
methodologies discussed herein.
DETAILED DESCRIPTION
[0013] The description that follows includes systems, methods,
techniques, instruction sequences, and computing machine program
products that embody illustrative embodiments of the disclosure. In
the following description, for the purposes of explanation,
numerous specific details are set forth in order to provide an
understanding of various embodiments of the inventive subject
matter. It will be evident, however, to those skilled in the art,
that embodiments of the inventive subject matter may be practiced
without these specific details. In general, well-known instruction
instances, protocols, structures, and techniques are not
necessarily shown in detail.
[0014] Aspects of the present disclosure include systems, methods,
techniques, instruction sequences, and computing machine program
products that allow a user to select an object, region, or other
element in an original image to be removed and replaced using other
portions (e.g. background) of the image, thereby making the
resulting edited image appear more natural. As an example, a user
may take a picture of two people, and select one of the two people
for removal from the picture. Upon receiving an indication of the
selection of person to be removed, the system removes the selected
person from the image and inpaints (e.g., fills) the missing region
(e.g., the region with the person removed) using portions of the
picture near the missing region. The result of this process is an
edited picture of a single person that appears natural despite
omitting the second person that was in the original image.
[0015] Consistent with some embodiments, the system determines a
local region for a user-selected region that includes the object or
other element the user seeks to remove. The systems selects the
portions of the image to inpaint (e.g., fill) the user-selected
region from the local region. More specifically, the system
identifies patch matches within the local region (e.g., two
identical image patches that each comprise one or more pixels) and
uses a portion of the identified patch matches to inpaint the
user-selected region.
[0016] As part of this process, the system computes local patch
match statistics that comprise patch offsets for the identified
patch matches. The patch offsets include a distance (e.g.,
represented in two spatial dimensions) between patch matches. The
system uses the patch match statistics to build a spatial
histogram. To fill the user selected region, a pixel-level graph
cut algorithm may be applied, where the label of each pixel in the
user-selected region corresponds to a possible (x, y) offset in the
histogram. By computing patch match statistics only in the local
region rather than the entire image, the system achieves a faster
runtime speed compared to other methods that use patch match
statistics from the entire image. Additionally, by limiting the
computation of patch matches to the local region, the system
employs improved techniques for image inpainting that overcome
difficulties encountered by conventional methodologies in
processing complex backgrounds or cluttered scenes.
[0017] To incorporate sufficient patch match statistics when the
user-selected region is close to the image border, the system may
pad the original image by a predefined padding size (e.g., by
appending a reflection of the outer portion of the image to the
image border), and enlarge the local region by the predefined
padding size to allow sufficient patch match statistics to be
calculated. The system may further scale (e.g., resize) the
user-selected region to a predetermined size (e.g., 100.times.75
pixels) before filling the region with the portion of the
identified patch matches.
[0018] Additionally, in some instances, the techniques used to fill
the user-selected region with portions of the patch matches may
create strong edges (e.g., pronounced image brightness
discontinuities) in the inpainted image. To compensate for this,
the system applies edge-preserving filtering techniques to blur
insignificant edges. The system may then identify the strong edges
from the filtered image to generate an edge map. The system may
further dilate the edge map to generate a binary mask for possible
strong edge pixels. The system also applies blurring techniques
(e.g., Gaussian blur) on the inpainting result to generate a
blurred version of the inpainted image. The system may then blend
together the original inpainted image with the blurred version by
applying blending techniques (e.g., Laplacian blending) with the
binary mask. In this way, the system may produce high-quality
natural-looking inpainted images while being optimized for runtime
speed (especially on mobile configurations).
[0019] FIG. 1 is a block diagram showing an example messaging
system 100 for exchanging data (e.g., messages and associated
content) over a network. The messaging system 100 includes multiple
client devices 102, each of which hosts a number of applications
including a messaging client application 104. Each messaging client
application 104 is communicatively coupled to other instances of
the messaging client application 104 and a messaging server system
108 via a network 106 (e.g., the Internet). As used herein, the
term "client device" may refer to any machine that interfaces with
a communications network (such as the network 106) to obtain
resources from one or more server systems or other client devices.
A client device may be, but is not limited to, a mobile phone,
desktop computer, laptop, portable digital assistant (PDA), smart
phone, tablet, ultra book, netbook, laptop, multi-processor system,
microprocessor-based or programmable consumer electronics system,
game console, set-top box, or any other communication device that a
user may use to access a network.
[0020] In the example shown in FIG. 1, each messaging client
application 104 is able to communicate and exchange data with
another messaging client application 104 and with the messaging
server system 108 via the network 106. The data exchanged between
the messaging client applications 104, and between a messaging
client application 104 and the messaging server system 108,
includes functions (e.g., commands to invoke functions) as well as
payload data (e.g., text, audio, video, or other multimedia
data).
[0021] The network 106 may include, or operate in conjunction with,
an ad hoc network, an intranet, an extranet, a virtual private
network (VPN), a local area network (LAN), a wireless LAN (WLAN), a
wide area network (WAN), a wireless WAN (WWAN), a metropolitan area
network (MAN), the Internet, a portion of the Internet, a portion
of the Public Switched Telephone Network (PSTN), a plain old
telephone service (POTS) network, a cellular telephone network, a
wireless network, a Wi-Fi.RTM. network, another type of network, or
a combination of two or more such networks. For example, the
network 106 or a portion of the network 106 may include a wireless
or cellular network and the connection to the network 106 may be a
Code Division Multiple Access (CDMA) connection, a Global System
for Mobile communications (GSM) connection, or another type of
cellular or wireless coupling. In this example, the coupling may
implement any of a variety of types of data transfer technology,
such as Single Carrier Radio Transmission Technology (1.times.RTT),
Evolution-Data Optimized (EVDO) technology, General Packet Radio
Service (GPRS) technology, Enhanced Data rates for GSM Evolution
(EDGE) technology, third-Generation Partnership Project (3GPP)
including 3G, fourth-generation wireless (4G) networks, Universal
Mobile Telecommunications System (UMTS), High-Speed Packet Access
(HSPA), Worldwide Interoperability for Microwave Access (WiMAX),
Long-Term Evolution (LTE) standard, or others defined by various
standard-setting organizations, other long-range protocols, or
other data transfer technology.
[0022] The messaging server system 108 provides server-side
functionality via the network 106 to a particular messaging client
application 104. While certain functions of the messaging system
100 are described herein as being performed by either a messaging
client application 104 or by the messaging server system 108, it
will be appreciated that the location of certain functionality
either within the messaging client application 104 or the messaging
server system 108 is a design choice. For example, it may be
technically preferable to initially deploy certain technology and
functionality within the messaging server system 108, but to later
migrate this technology and functionality to the messaging client
application 104 where a client device 102 has a sufficient
processing capacity.
[0023] The messaging server system 108 supports various services
and operations that are provided to the messaging client
application 104. Such operations include transmitting data to,
receiving data from, and processing data generated by the messaging
client application 104. This data may include message content,
client device information, geolocation information, media
annotation and overlays, message content persistence conditions,
social network information, and live event information, as
examples. Data exchanges within the messaging system 100 are
invoked and controlled through functions available via user
interfaces (UIs) of the messaging client application 104.
[0024] Turning now specifically to the messaging server system 108,
an Application Programming Interface (API) server 110 is coupled
to, and provides a programmatic interface to, an application server
112. The application server 112 is communicatively coupled to a
database server 118, which facilitates access to a database 120 in
which is stored data associated with messages processed by the
application server 112.
[0025] The API server 110 receives and transmits message data
(e.g., commands and message payloads) between the client device 102
and the application server 112. Specifically, the API server 110
provides a set of interfaces (e.g., routines and protocols) that
can be called or queried by the messaging client application 104 in
order to invoke functionality of the application server 112. The
API server 110 exposes various functions supported by the
application server 112, including account registration; login
functionality; the sending of messages, via the application server
112, from a particular messaging client application 104 to another
messaging client application 104; the sending of media files (e.g.,
images or video) from a messaging client application 104 to the
application server 112, for possible access by another messaging
client application 104; the setting of a collection of media data
(e.g., story); the retrieval of a list of friends of a user of a
client device 102; the retrieval of such collections; the retrieval
of messages and content; the adding and deletion of friends to and
from a social graph; the location of friends within a social graph;
and the detecting of an application event (e.g., relating to the
messaging client application 104).
[0026] The application server 112 hosts a number of applications
and subsystems, including a messaging server application 114 and a
social network system 116. The messaging server application 114
implements a number of message processing technologies and
functions, particularly related to the aggregation and other
processing of content (e.g., textual and multimedia content)
included in messages received from multiple instances of the
messaging client application 104. As will be described in further
detail, the text and media content from multiple sources may be
aggregated into collections of content (e.g., called stories or
galleries). These collections are then made available, by the
messaging server application 114, to the messaging client
application 104. Other processor- and memory-intensive processing
of data may also be performed server-side by the messaging server
application 114, in view of the hardware requirements for such
processing.
[0027] The social network system 116 supports various social
networking functions and services, and makes these functions and
services available to the messaging server application 114. To this
end, the social network system 116 maintains and accesses an entity
graph within the database 120. Examples of functions and services
supported by the social network system 116 include the
identification of other users of the messaging system 100 with whom
a particular user has relationships or whom the user is
"following," and also the identification of other entities and
interests of a particular user.
[0028] FIG. 2 is block diagram illustrating further details
regarding the messaging system 100, according to exemplary
embodiments. Specifically, the messaging system 100 is shown to
comprise the messaging client application 104 and the application
server 112, which in turn embody a number of subsystems, namely an
ephemeral timer system 202, a collection management system 204, an
annotation system 206, and an image processing system 208.
[0029] The ephemeral timer system 202 is responsible for enforcing
the temporary access to content permitted by the messaging client
application 104 and the messaging server application 114. To this
end, the ephemeral timer system 202 incorporates a number of timers
that, based on duration and display parameters associated with a
message, or collection of messages (e.g., a SNAPCHAT story),
selectively display and enable access to messages and associated
content via the messaging client application 104. Further details
regarding the operation of the ephemeral timer system 202 are
provided below.
[0030] The collection management system 204 is responsible for
managing collections of media (e.g., collections of text, image,
video, and audio data). In some examples, a collection of content
(e.g., messages, including images, video, text, and audio) may be
organized into an "event gallery" or an "event story." Such a
collection may be made available for a specified time period, such
as the duration of an event to which the content relates. For
example, content relating to a music concert may be made available
as a "story" for the duration of that music concert. The collection
management system 204 may also be responsible for publishing an
icon that provides notification of the existence of a particular
collection to the user interface of the messaging client
application 104.
[0031] The collection management system 204 furthermore includes a
curation interface 210 that allows a collection manager to manage
and curate a particular collection of content. For example, the
curation interface 210 enables an event organizer to curate a
collection of content relating to a specific event (e.g., delete
inappropriate content or redundant messages). Additionally, the
collection management system 204 employs machine vision (or image
recognition technology) and content rules to automatically curate a
content collection. In certain embodiments, compensation may be
paid to a user for inclusion of user-generated content in a
collection. In such cases, the curation interface 210 operates to
automatically make payments to such users for the use of their
content.
[0032] The annotation system 206 provides various functions that
enable a user to annotate or otherwise modify or edit media content
associated with a message. For example, the annotation system 206
provides functions related to the generation and publishing of
media overlays for messages processed by the messaging system 100.
For example, the annotation system 206 operatively supplies a media
overlay (e.g., a SNAPCHAT filter) to the messaging client
application 104 based on a geolocation of the client device 102. In
another example, the annotation system 206 operatively supplies a
media overlay to the messaging client application 104 based on
other information, such as social network information of the user
of the client device 102. A media overlay may include audio and
visual content and visual effects. Examples of audio and visual
content include pictures, texts, logos, animations, and sound
effects. An example of a visual effect includes color overlaying.
The audio and visual content or the visual effects can be applied
to a media content item (e.g., a photo) at the client device 102.
For example, the media overlay may include text that can be
overlaid on top of a photograph generated by the client device 102.
In another example, the media overlay includes an identification of
a location (e.g., Venice Beach), a name of a live event, or a name
of a merchant (e.g., Beach Coffee House). In another example, the
annotation system 206 uses the geolocation of the client device 102
to identify a media overlay that includes the name of a merchant at
the geolocation of the client device 102. The media overlay may
include other indicia associated with the merchant. The media
overlays may be stored in the database 120 and accessed through the
database server 118.
[0033] In one exemplary embodiment, the annotation system 206
provides a user-based publication platform that enables users to
select a geolocation on a map, and upload content associated with
the selected geolocation. The user may also specify circumstances
under which a particular media overlay should be offered to other
users. The annotation system 206 generates a media overlay that
includes the uploaded content and associates the uploaded content
with the selected geolocation.
[0034] In another exemplary embodiment, the annotation system 206
provides a merchant-based publication platform that enables
merchants to select a particular media overlay associated with a
geolocation via a bidding process. For example, the annotation
system 206 associates the media overlay of a highest-bidding
merchant with a corresponding geolocation for a predefined amount
of time.
[0035] The image processing system 208 is dedicated to performing
various image processing operations, in some instances, with
respect to images or video received within the payload of a message
at the messaging server application 114. As an example, the image
processing system 208 provides functionality to allow a user to
select an object or other element in an original image to be
removed and replaced using other portions of the image. Further
details regarding the image processing system 208 are discussed
below in reference to FIG. 4, according to some embodiments.
[0036] FIG. 3 is a schematic diagram 300 illustrating data which
may be stored in the database 120 of the messaging server system
108, according to certain exemplary embodiments. While the content
of the database 120 is shown to comprise a number of tables, it
will be appreciated that the data could be stored in other types of
data structures (e.g., as an object-oriented database).
[0037] The database 120 includes message data stored within a
message table 314. An entity table 302 stores entity data,
including an entity graph 304. Entities for which records are
maintained within the entity table 302 may include individuals,
corporate entities, organizations, objects, places, events, etc.
Regardless of type, any entity regarding which the messaging server
system 108 stores data may be a recognized entity. Each entity is
provided with a unique identifier, as well as an entity type
identifier (not shown).
[0038] The entity graph 304 furthermore stores information
regarding relationships and associations between or among entities.
Such relationships may be social, professional (e.g., work at a
common corporation or organization), interested-based, or
activity-based, merely for example.
[0039] The database 120 also stores annotation data, in the example
form of filters, in an annotation table 312. Filters for which data
is stored within the annotation table 312 are associated with and
applied to videos (for which data is stored in a video table 310)
and/or images (for which data is stored in an image table 308).
Filters, in one example, are overlays that are displayed as
overlaid on an image or video during presentation to a recipient
user. Filters may be of varies types, including user-selected
filters from a gallery of filters presented to a sending user by
the messaging client application 104 when the sending user is
composing a message. Other types of filters include geolocation
filters (also known as geo-filters), which may be presented to a
sending user based on geographic location. For example, geolocation
filters specific to a neighborhood or special location may be
presented within a user interface by the messaging client
application 104, based on geolocation information determined by a
Global Positioning System (GPS) unit of the client device 102.
Another type of filter is a data filter, which may be selectively
presented to a sending user by the messaging client application
104, based on other inputs or information gathered by the client
device 102 during the message creation process. Examples of data
filters include a current temperature at a specific location, a
current speed at which a sending user is traveling, a battery life
for a client device 102, or the current time.
[0040] Other annotation data that may be stored within the image
table 308 is so-called "lens" data. A "lens" may be a real-time
special effect and sound that may be added to an image or a
video.
[0041] As mentioned above, the video table 310 stores video data
which, in one embodiment, is associated with messages for which
records are maintained within the message table 314. Similarly, the
image table 308 stores image data associated with messages for
which message data is stored in the entity table 302. The entity
table 302 may associate various annotations from the annotation
table 312 with various images and videos stored in the image table
308 and the video table 310.
[0042] A story table 306 stores data regarding collections of
messages and associated image, video, or audio data, which are
compiled into a collection (e.g., a SNAPCHAT story or a gallery).
The creation of a particular collection may be initiated by a
particular user (e.g., a user for whom a record is maintained in
the entity table 302). A user may create a "personal story" in the
form of a collection of content that has been created and
sent/broadcast by that user. To this end, the user interface of the
messaging client application 104 may include an icon that is
user-selectable to enable a sending user to add specific content to
his or her personal story.
[0043] A collection may also constitute a "live story," which is a
collection of content from multiple users that is created manually,
automatically, or using a combination of manual and automatic
techniques. For example, a "live story" may constitute a curated
stream of user-submitted content from various locations and events.
Users whose client devices have location services enabled and who
are at a common location or event at a particular time may, for
example, be presented with an option, via a user interface of the
messaging client application 104, to contribute content to a
particular live story. The live story may be identified to the user
by the messaging client application 104, based on his or her
location. The end result is a "live story" told from a community
perspective.
[0044] A further type of content collection is known as a "location
story," which enables a user whose client device 102 is located
within a specific geographic location (e.g., on a college or
university campus) to contribute to a particular collection. In
some embodiments, a contribution to a location story may require a
second degree of authentication to verify that the end user belongs
to a specific organization or other entity (e.g., is a student on
the university campus).
[0045] FIG. 4 is a block diagram illustrating functional components
of the image processing system 208 that forms part of the messaging
system 100, according to some example embodiments. To avoid
obscuring the inventive subject matter with unnecessary detail,
various functional components (e.g., modules, engines, and
databases) that are not germane to conveying an understanding of
the inventive subject matter have been omitted from FIG. 4.
However, a skilled artisan will readily recognize that various
additional functional components may be supported by the image
processing system 208 to facilitate additional functionality that
is not specifically described herein. As shown, the image
processing system 208 includes a preprocessing component 402, an
inpainting component 404, and a smoothing component 406. The above
referenced functional components of the image processing system 208
are configured to communicate with each other (e.g., via a bus,
shared memory, a switch, or APIs). Collectively, these components
facilitate inpainting of a user-selected region of an image using
local patch matches in the image. In other words, the preprocessing
component 402, inpainting component 404, and smoothing component
406 work in conjunction to allow a user to select an object or
other element in an original image to be removed and replaced using
other portions of the image, thereby making the resulting modified
image without the object or other element appear natural.
[0046] The preprocessing component 402 is responsible for
performing various transformations to images prior to inpainting to
improve (e.g., optimize) runtime speed of the inpainting. To this
end, the preprocessing component 402 is configured to determine a
local region in an image that serves as a boundary to limit the
computations involved in the inpainting process to a neighboring
region surrounding the user-selected region of the image. A size of
the local region is dynamically computed based on a size of the
user-selected region. The preprocessing component 402 is further
configured to pad the height and width dimensions of the image by a
predetermined padding size to incorporate enough background to
calculate patch match statistics when the user-selected region is
close to the image border. Additionally, the preprocessing
component 402 may further enlarge the local region by the
predetermined padding size, and scale (e.g., resize) the
user-selected region to a predetermined size (e.g., 100.times.75
pixels) as part of the runtime speed optimization.
[0047] The inpainting component 404 is configured to inpaint the
user-selected region using local patch match statistics. As part of
this process, the inpainting component 404 identifies patch matches
(e.g., identical groupings of pixels) in the local region and
obtains the offsets of the patch matches (e.g., a distance between
patch matches defined by two-dimensional coordinates). The
inpainting component 404 inpaints the user-selected region using at
least a portion of the patch matches from the local region. More
specifically, the inpainting component 404 fills the user-selected
region by combining a stack of shift images based on patch offset
statistics computed for the patch matches in the local region. The
inpainting of the user-selected region in the original image
results in a modified image that appears natural despite omitting
what was previously shown in the user-selected region.
[0048] The smoothing component 406 is configured to blend the
inpainted region into the image and smooth any strong edges (e.g.,
pronounced image brightness discontinuities) resulting from the
inpainting process. For conventional methodologies, the final image
blending step can be a speed bottleneck, especially in mobile
configurations and/or when the user-selected region is large. To
improve upon conventional methodologies, the smoothing component
406 may apply fast and lightweight blending techniques such as
Laplacian blending to the inpainted region to optimize runtime
speed. Additionally, as noted above, the inpainting process may, in
some instances, introduce strong edges into the resulting modified
image. To smooth these strong edges, the smoothing component 406
applies edge-preserving filtering to the initial inpainting result
(e.g., the modified image produced by the inpainting component 404)
to blur insignificant edges, and the smoothing component 406 then
identifies strong edges in the resulting filtered grayscale image
to produce an edge map. The smoothing component 406 may further
dilate the resulting edge map to generate a binary mask for
possible strong edge pixels. The smoothing component 406 also
applies blurring techniques (e.g., Gaussian blur) on the inpainting
result to generate a blurred version of the image, which is blended
together with the initial inpainting result by applying blending
techniques (e.g., Laplacian blending) with the binary mask
mentioned above.
[0049] As is understood by skilled artisans in the relevant
computer and Internet-related arts, each functional component
illustrated in FIG. 4 may be implemented using hardware (e.g., a
processor of a machine) or a combination of logic (e.g., executable
software instructions) and hardware (e.g., memory and the processor
of a machine) for executing the logic. For example, any component
included as part of the image processing system 208 may physically
include an arrangement of one or more processors 408 (e.g., a
subset of or among one or more processors of a machine) configured
to perform the operations described herein for that component. As
another example, any component of the image processing system 208
may include software, hardware, or both, that configure an
arrangement of the one or more processors 408 to perform the
operations described herein for that component. Accordingly,
different components of the image processing system 208 may include
and configure different arrangements of such processors 408 or a
single arrangement of such processors 408 at different points in
time.
[0050] Furthermore, the various functional components depicted in
FIG. 4 may reside on a single machine (e.g., a client device or a
server) or may be distributed across several machines in various
arrangements such as cloud-based architectures. Moreover, any two
or more of these components may be combined into a single
component, and the functions described herein for a single
component may be subdivided among multiple components. Functional
details of these components are described below with respect to
FIGS. 5-8.
[0051] FIGS. 5-8 are flow charts illustrating operations of the
image processing system 208 in performing an example method 500 for
digital image editing, according to some embodiments. The method
500 may be embodied in computer-readable instructions for execution
by one or more processors such that the operations of the method
500 may be performed in part or in whole by the functional
components of the image processing system 208; accordingly, the
method 500 is described below by way of example with reference
thereto. However, it shall be appreciated that at least some of the
operations of the method 500 may be deployed on various other
hardware configurations and the method 500 is not intended to be
limited to the image processing system 208.
[0052] In the context of method 500, the image processing system
208 accesses an image stored on the client device 102 or at the
messaging server system 108. The image may be displayed within or
as part of a user interface provided by the messaging client
application 104 for presentation on the client device 102, and in
some instances, the image may be captured by the client device
102.
[0053] At operation 505, the image processing system 208 receives a
user input identifying a user-selected region of the image. The
user may provide the user input identifying the selected region of
the image by tracing a border of the region on the image by way of
appropriate interaction with an input device of the client device
102 (e.g., using a finger to trace the border on a touch screen of
the client device 102). Accordingly, the user-selected region of
the image is not limited to any particular shape or size. For
purposes of clarity in describing the method 500, the image on
which the user selects the region may be referred to as the
"original image."
[0054] At operation 510, the preprocessing component 402 determines
a local region for the user-selected region of the original image
based on a size of the user-selected region. The local region
includes a portion of the original image outside of the
user-selected region and that surrounds the user-selected region.
The determining of the local region includes dynamically computing
a size (e.g., height and width) of the local region based on a size
of the user-selected region. For example, if the height of the
user-selected region is h and the width of the user-selected region
is w, the preprocessing component 402 may compute the height of the
local region to be 2h and the width to be 2w.
[0055] At operation 515, the preprocessing component 402 enlarges
the local region of the original image by a predefined padding
size. For example, the preprocessing component 402 may enlarge the
height, h, of the local region by h/2, and the width, w, by
w/2.
[0056] At operation 520, the preprocessing component 402 scales
(e.g., resizes) the user-selected region of the original image to a
predetermined size. For example, the preprocessing component 402
may down scale the user-selected region by resizing it to a
predetermined size of 100 pixels.times.75 pixels. The scaling of
the user-selected region yields a scaled user-selected region.
[0057] At operation 525, the preprocessing component 402 pads the
original image by the predefined padding size. In padding the
original image, the preprocessing component 402 copies an outer
boundary of the original image, and appends the copied outer
boundary to the original image border such that the appended copy
of the outer boundary creates a mirrored reflection of the actual
outer boundary. As an example, the preprocessing component 402 may
pad the height, h, of the original image by h/2, and the width, w,
by w/2.
[0058] At operation 530, the inpainting component 404 computes a
binary mask for the scaled user-selected region. In computing the
binary mask, the inpainting component 404 marks pixels inside the
scaled user-selected region as "1," and marks pixels in the
remainder of the image as "0."
[0059] At operation 535, the inpainting component 404 identifies
patch matches within the enlarged local region. Each patch match
comprises two identical image patches, and each image patch
comprises one or more pixels of the original image. By limiting the
search for patch matches to the enlarged local region rather than
the entire image, the inpainting component 404 may achieve a faster
runtime speed.
[0060] To identify patch matches within the enlarged local region
of the original image, the inpainting component 404 may apply a
PatchMatch algorithm to the enlarged local region. The PatchMatch
algorithm finds the patch matches by defining a nearest-neighbor
field (NNF) as a function f: R.sup.2.fwdarw.R.sup.2 of offsets,
which is over all possible patch matches in the enlarged local
region, for some distance function D between two patches. The
algorithm comprises three main operations: 1) fill the NNF with
either random offsets or some prior information; 2) apply an
iterative update process to the NNF, in which good patch offsets
are propagated to adjacent pixels; and 3) perform a random search
in the neighborhood of the best offset found so far. Independently
of these three operations, the PatchMatch algorithm may also use a
coarse-to-fine approach by building an image pyramid to obtain a
better result.
[0061] At operation 540, the inpainting component 404 inpaints
(e.g., fills) the masked region (e.g., the scaled user-selected
region) in the original image using a portion of the identified
patch matches. The inpainting of the masked region in the original
image results in a modified image where the user-selected region
has been filled with other portions of the image to produce an
image that appears natural despite omitting what was previously
shown in the user-selected region.
[0062] At operation 545, the smoothing component 406 blends the
inpainted region into the modified image. For example, the
smoothing component 406 may apply Laplacian blending to blend the
inpainted region into the modified image. The blending of the
inpainted region results in a modified image that appears even more
natural than the initial inpainted result produced as a result of
operation 540.
[0063] As shown in FIG. 6, the method 500 may, in some embodiments,
also include operations 605, 610, 615, and 620. The operations 605
and 610 may be performed subsequent to operation 535, in which the
inpainting component 404 identifies patch matches within the
enlarged local region, or as part of the operation 540, in which
the inpainting component 404 inpaints the user-selected region in
the original image using a portion of the patch match offsets. At
operation 605, the inpainting component 404 computes patch offsets
for the patch matches identified, at operation 535, within the
enlarged local region. A patch offset includes two-dimensional
coordinates representing a distance between two patch matches. As
an example, for each patch, P, in the enlarged local region, the
inpainting component 404 computes its offset s to its most similar
patch according to the following function:
s(x)=arg min .parallel.P(x+s)-P(x)H.parallel..sup.2 s.t.
|s|>.tau..
Here, s=(u, v) is the two-dimensional coordinates of the offset,
x=(x, y) is the position of a patch, and P(x) is the patch centered
between two patches. The threshold .tau. is to preclude nearby
patches.
[0064] At operation 610, the inpainting component 404 determines
patch offset statistics based on the patch offsets. For example,
given all the offsets s(x), the inpainting component 404 computes
their statistics by a two-dimensional histogram h(u,v):
h(u,v)=.SIGMA..sub.x.delta.(s(x)=(u,v)).
Here, .delta.(.) is 1 when the argument is true and 0
otherwise.
[0065] Operation 615 may be performed as part of the operation 540,
in which the inpainting component 404 inpaints the user-selected
region using a portion of the identified patch matches. At
operation 615, the inpainting component 404 combines shifted images
based on the offset statistics to fill the scaled user-selected
region to produce the modified image. The combining of the shifted
images may include applying the pixel-level graph cut algorithm,
where the inpainting component 404 applies a label to each pixel in
the enlarged local region that corresponds to a possible offset in
the histogram while also enforcing pairwise consistency constraints
between two neighboring pixels.
[0066] For example, the inpainting component 404 may identify the K
dominant offsets in the two-dimensional histogram, which are the K
highest peaks in the histogram. Given the K offsets, the inpainting
component 404 optimizes the following Markov random field (MRF)
energy function:
E .function. ( L ) = x .di-elect cons. .OMEGA. .times. E d
.function. ( L .function. ( x ) ) + ( x , x ' ) | x .di-elect cons.
.OMEGA. , x ' .di-elect cons. .OMEGA. .times. E s .function. ( L
.function. ( x ) , L .function. ( x ' ) ) . ##EQU00001##
Here, Q is the user-selected region, and (x, x') are 4-connected
neighbors. L is a label representing the pre-selected offsets
{s.sub.i}.sup.K.sub.i=1 or s.sub.0=(0,0).sup.1. "L(x)=i" means that
the inpainting component 404 copies the pixel at x+5, to the
location x. The term E.sub.d is 0 if the label is valid (x+s.sub.i
is a known pixel), and if not it is +.infin.. The smoothness term
E.sub.s penalizes incoherent seams. When a=L(x) and b=L(x'),
E.sub.s is defined as:
E.sub.s(a,b)=.parallel.(x+s.sub.a)-I(x+s.sub.b).parallel..sup.2+.paralle-
l.I(x'+s.sub.a)-I(x'+s.sub.b).parallel..sup.2.
Here, I(x) is the RGB color of x. I(x+s) is a shifted image given
fixed s. If s.sub.a.noteq.s.sub.b there is a seam between x and x'.
Thus, the above equation penalizes such a seam that the two shifted
images I(x+s.sub.a) and I(x+s.sub.b) are not similar near this
seam.
[0067] Operation 620 may be performed subsequent to operation 540,
in which the inpainting component 404 inpaints the user selected
region in the original image using a portion of the identified
patch matches. At operation 620, the inpainting component 404
upscales the inpainted region to the original image resolution. For
example, the inpainting component 404 may employ a super resolution
step using the graph cut algorithm. To illustrate this example, the
following function may be used:
E(X)=.SIGMA..sub.i.PHI.(xi)+.SIGMA..sub.j in N(xi).PSI.(xi,xj)
Here, x is a set of known pixels and N(x.sub.i) is a neighborhood
of unknown pixels x.sub.i after applying transform to its closest
inpainted pixel. For each unknown pixel (x.sub.i, y.sub.i), the
inpainting component finds the closest inpainted pixel (x.sub.0,
y.sub.0) and then applies a (x, y) transform to get the new pixel
location (x.sub.0+x, y.sub.0+y), then find a 4-pixel neighborhood
of the pixel location (x.sub.0+x-(x.sub.i-x.sub.0),
y.sub.0+y-(y.sub.i-y.sub.0)). The graph cut algorithm then assigns
one of these four pixel values to (x.sub.i, y.sub.i), based on
color intensity matching score as well as a neighboring pixel
consistency constraint. To further optimize the runtime speed on
mobile devices, the image processing system 208 may constrain the
search neighborhood such that each pixel in the low-resolution
result can only map back to its top left or bottom right neighbors
(rather than all four neighboring pixels).
[0068] As shown in FIG. 7, the method 500 may also include
operations 705, 710, 715, 720, 725, 730, and 735, which may be
performed subsequently to the operation 535, in which the
inpainting component 404 identifies patch matches within the
enlarged local region. At operation 705, the inpainting component
404 computes patch offset statistics in the manner described above
with reference to operation 610.
[0069] At operation 710, the inpainting component 404 determines a
mean and standard deviation of the offsets in the histogram
described above in reference to operation 610. At operation 715,
the inpainting component 404 determines a ratio between the mean
and standard deviation. At operation 720, the inpainting component
404 determines whether the ratio is above a predefined threshold.
If, at operation 720, the inpainting component 404 determines that
the ratio is above the threshold, the method 500 proceeds to
operation 540, where the inpainting component 404 inpaints the
user-selected region in the original image using a portion of the
identified patch matches.
[0070] If, at operation 720, the inpainting component 404
determines that the ratio is not above the threshold, the method
500 proceeds to operation 725, where the inpainting component 404
computes a mean color of neighboring pixels (e.g., pixels near the
user-selected region). At operation 730, the inpainting component
404 generates a color mask using the mean color of the neighboring
pixels. At operation 735, the inpainting component 404 fills the
user-selected region using the color mask.
[0071] As shown in FIG. 8, the method 500 may, in some embodiments,
include operations 805, 810, 815, 820, and 825. Operations 805,
810, 815, and 820 may be performed subsequently to operation 540,
in which the inpainting component 404 inpaints the user-selected
region using a portion of the identified patch matches, thereby
generating the modified image. At operation 805, the smoothing
component 406 identifies one or more strong edges in the modified
image. The smoothing component 406 may identify the strong edges by
applying edge-preserving filtering to the modified image to blur
insignificant edges in the modified image. The application of the
edge-preserving filtering results in a grayscale image.
[0072] At operation 810, the smoothing component 406 generates an
edge map for the modified image based on the grayscale image. At
operation 815, the smoothing component 406 generates a binary mask
for possible strong edges in the modified image. The smoothing
component 406 may generate the binary mask by dilating the edge
map.
[0073] At operation 820, the smoothing component 406 applies
blurring techniques to the modified image to produce a blurred
version of the modified image (referred to hereinafter as the
"blurred image"). For example, the smoothing component 406 may
apply Gaussian blurring to the modified image to produce the
blurred image.
[0074] Operation 825 may be performed in parallel with or as part
of operation 545, in which the smoothing component 406 blends the
inpainted region into the modified image. At operation 825, the
smoothing component 406 blends the modified image with the blurred
image using the binary mask to produce an further modified image
with no strong edges. In blending the modified image with the
blurred image, the smoothing component 406 may apply Laplacian
blending.
[0075] FIGS. 9A and 9B are interface diagrams illustrating aspects
of user interfaces provided by the messaging system, according to
some embodiments. In particular, FIG. 9A illustrates an original
image 900 that may be captured by and presented within a user
interface display on the client device 102. The original image 900
includes a user-selected region 902. As an example, a user of the
client device 102 may select the region 902 by using his or her
finger to trace an outline of the region 902 on a touch screen of
the client device 102, although any other appropriate input device
(e.g., a mouse) may be used to trace an outline of the region 902.
An object, specifically a sign, is shown within the user-selected
region 902.
[0076] FIG. 9B illustrates a modified image 950 that may be
presented within a user interface display on the client device 102.
The modified image 950 is an edited version of the original image
900 generated by applying the techniques described herein to the
user-selected region 902. More specifically, in the modified image
950 the user-selected region 902 has been removed and replaced with
other portions of the original image 900 to create a
natural-looking image without the object shown within the
user-selected region 902 of the original image 900.
Software Architecture
[0077] FIG. 10 is a block diagram illustrating an example software
architecture 1006, which may be used in conjunction with various
hardware architectures herein described. FIG. 10 is a non-limiting
example of a software architecture and it will be appreciated that
many other architectures may be implemented to facilitate the
functionality described herein. The software architecture 1006 may
execute on hardware such as a machine 1100 of FIG. 11 that
includes, among other things, processors 1104, memory/storage 1106,
and I/O components 1118. A representative hardware layer 1052 is
illustrated and can represent, for example, the machine 1100 of
FIG. 11. The representative hardware layer 1052 includes a
processing unit 1054 having associated executable instructions
1004. The executable instructions 1004 represent the executable
instructions of the software architecture 1006, including
implementation of the methods, components, and so forth described
herein. The hardware layer 1052 also includes memory and/or storage
modules memory/storage 1056, which also have the executable
instructions 1004. The hardware layer 1052 may also comprise other
hardware 1058.
[0078] As used herein, the term "component" may refer to a device,
a physical entity, or logic having boundaries defined by function
or subroutine calls, branch points, APIs, and/or other technologies
that provide for the partitioning or modularization of particular
processing or control functions. Components may be combined via
their interfaces with other components to carry out a machine
process. A component may be a packaged functional hardware unit
designed for use with other components and a part of a program that
usually performs a particular function of related functions.
[0079] Components may constitute either software components (e.g.,
code embodied on a machine-readable medium) or hardware components.
A "hardware component" is a tangible unit capable of performing
certain operations and may be configured or arranged in a certain
physical manner. In various exemplary embodiments, one or more
computer systems (e.g., a standalone computer system, a client
computer system, or a server computer system) or one or more
hardware components of a computer system (e.g., a processor or a
group of processors) may be configured by software (e.g., an
application or application portion) as a hardware component that
operates to perform certain operations as described herein. A
hardware component may also be implemented mechanically,
electronically, or any suitable combination thereof. For example, a
hardware component may include dedicated circuitry or logic that is
permanently configured to perform certain operations.
[0080] A hardware component may be a special-purpose processor,
such as a Field-Programmable Gate Array (FPGA) or an
Application-Specific Integrated Circuit (ASIC). A hardware
component may also include programmable logic or circuitry that is
temporarily configured by software to perform certain operations.
For example, a hardware component may include software executed by
a general-purpose processor or other programmable processor. Once
configured by such software, hardware components become specific
machines (or specific components of a machine) uniquely tailored to
perform the configured functions and are no longer general-purpose
processors. It will be appreciated that the decision to implement a
hardware component mechanically, in dedicated and permanently
configured circuitry, or in temporarily configured circuitry (e.g.,
configured by software) may be driven by cost and time
considerations.
[0081] A processor may be, or include, any circuit or virtual
circuit (a physical circuit emulated by logic executing on an
actual processor) that manipulates data values according to control
signals (e.g., "commands," "op codes," "machine code," etc.) and
that produces corresponding output signals that are applied to
operate a machine. A processor may, for example, be a Central
Processing Unit (CPU), a Reduced Instruction Set Computing (RISC)
processor, a Complex Instruction Set Computing (CISC) processor, a
Graphics Processing Unit (GPU), a Digital Signal Processor (DSP),
an ASIC, a Radio-Frequency Integrated Circuit (RFIC), or any
combination thereof. A processor may further be a multi-core
processor having two or more independent processors (sometimes
referred to as "cores") that may execute instructions
contemporaneously.
[0082] Accordingly, the phrase "hardware component" (or
"hardware-implemented component") should be understood to encompass
a tangible entity, be that an entity that is physically
constructed, permanently configured (e.g., hardwired), or
temporarily configured (e.g., programmed) to operate in a certain
manner or to perform certain operations described herein.
Considering embodiments in which hardware components are
temporarily configured (e.g., programmed), each of the hardware
components need not be configured or instantiated at any one
instance in time. For example, where a hardware component comprises
a general-purpose processor configured by software to become a
special-purpose processor, the general-purpose processor may be
configured as respectively different special-purpose processors
(e.g., comprising different hardware components) at different
times. Software accordingly configures a particular processor or
processors, for example, to constitute a particular hardware
component at one instance of time and to constitute a different
hardware component at a different instance of time. Hardware
components can provide information to, and receive information
from, other hardware components. Accordingly, the described
hardware components may be regarded as being communicatively
coupled. Where multiple hardware components exist
contemporaneously, communications may be achieved through signal
transmission (e.g., over appropriate circuits and buses) between or
among two or more of the hardware components. In embodiments in
which multiple hardware components are configured or instantiated
at different times, communications between or among such hardware
components may be achieved, for example, through the storage and
retrieval of information in memory structures to which the multiple
hardware components have access.
[0083] For example, one hardware component may perform an operation
and store the output of that operation in a memory device to which
it is communicatively coupled. A further hardware component may
then, at a later time, access the memory device to retrieve and
process the stored output. Hardware components may also initiate
communications with input or output devices, and can operate on a
resource (e.g., a collection of information). The various
operations of example methods described herein may be performed, at
least partially, by one or more processors that are temporarily
configured (e.g., by software) or permanently configured to perform
the relevant operations. Whether temporarily or permanently
configured, such processors may constitute processor-implemented
components that operate to perform one or more operations or
functions described herein. As used herein, "processor-implemented
component" refers to a hardware component implemented using one or
more processors. Similarly, the methods described herein may be at
least partially processor-implemented, with a particular processor
or processors being an example of hardware. For example, at least
some of the operations of a method may be performed by one or more
processors or processor-implemented components.
[0084] Moreover, the one or more processors may also operate to
support performance of the relevant operations in a "cloud
computing" environment or as a "software as a service" (SaaS). For
example, at least some of the operations may be performed by a
group of computers (as examples of machines including processors),
with these operations being accessible via a network (e.g., the
Internet) and via one or more appropriate interfaces (e.g., an
API). The performance of certain of the operations may be
distributed among the processors, not only residing within a single
machine, but deployed across a number of machines. In some
exemplary embodiments, the processors or processor-implemented
components may be located in a single geographic location (e.g.,
within a home environment, an office environment, or a server
farm). In other exemplary embodiments, the processors or
processor-implemented components may be distributed across a number
of geographic locations.
[0085] In the exemplary architecture of FIG. 10, the software
architecture 1006 may be conceptualized as a stack of layers where
each layer provides particular functionality. For example, the
software architecture 1006 may include layers such as an operating
system 1002, libraries 1020, frameworks/middleware 1018,
applications 1016, and a presentation layer 1014. Operationally,
the applications 1016 and/or other components within the layers may
invoke API calls 1008 through the software stack and receive a
response as messages 1010. The layers illustrated are
representative in nature and not all software architectures have
all layers. For example, some mobile or special-purpose operating
systems may not provide a frameworks/middleware 1018, while others
may provide such a layer. Other software architectures may include
additional or different layers.
[0086] The operating system 1002 may manage hardware resources and
provide common services. The operating system 1002 may include, for
example, a kernel 1022, services 1024, and drivers 1026. The kernel
1022 may act as an abstraction layer between the hardware and the
other software layers. For example, the kernel 1022 may be
responsible for memory management, processor management (e.g.,
scheduling), component management, networking, security settings,
and so on. The services 1024 may provide other common services for
the other software layers. The drivers 1026 are responsible for
controlling or interfacing with the underlying hardware. For
instance, the drivers 1026 include display drivers, camera drivers,
Bluetooth.RTM. drivers, flash memory drivers, serial communication
drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi.RTM.
drivers, audio drivers, power management drivers, and so forth
depending on the hardware configuration.
[0087] The libraries 1020 provide a common infrastructure that is
used by the applications 1016 and/or other components and/or
layers. The libraries 1020 provide functionality that allows other
software components to perform tasks in an easier fashion than by
interfacing directly with the underlying operating system 1002
functionality (e.g., kernel 1022, services 1024, and/or drivers
1026). The libraries 1020 may include system libraries 1044 (e.g.,
C standard library) that may provide functions such as memory
allocation functions, string manipulation functions, mathematical
functions, and the like. In addition, the libraries 1020 may
include API libraries 1046 such as media libraries (e.g., libraries
to support presentation and manipulation of various media formats
such as MPEG4, H.264, MP3, AAC, AMR, JPG, and PNG), graphics
libraries (e.g., an OpenGL framework that may be used to render 2D
and 3D graphic content on a display), database libraries (e.g.,
SQLite that may provide various relational database functions), web
libraries (e.g., WebKit that may provide web browsing
functionality), and the like. The libraries 1020 may also include a
wide variety of other libraries 1048 to provide many other APIs to
the applications 1016 and other software components/modules.
[0088] The frameworks/middleware 1018 provide a higher-level common
infrastructure that may be used by the applications 1016 and/or
other software components/modules. For example, the
frameworks/middleware 1018 may provide various graphic user
interface (GUI) functions, high-level resource management,
high-level location services, and so forth. The
frameworks/middleware 1018 may provide a broad spectrum of other
APIs that may be utilized by the applications 1016 and/or other
software components/modules, some of which may be specific to a
particular operating system 1002 or platform.
[0089] The applications 1016 include built-in applications 1038
and/or third-party applications 1040. Examples of representative
built-in applications 1038 may include, but are not limited to, a
contacts application, a browser application, a book reader
application, a location application, a media application, a
messaging application, and/or a game application. The third-party
applications 1040 may include an application developed using the
ANDROID.TM. or IOS.TM. software development kit (SDK) by an entity
other than the vendor of the particular platform, and may be mobile
software running on a mobile operating system such as IOS.TM.,
ANDROID.TM., WINDOWS.RTM. Phone, or other mobile operating systems.
The third-party applications 1040 may invoke the API calls 1008
provided by the mobile operating system (such as the operating
system 1002) to facilitate functionality described herein.
[0090] The applications 1016 may use built-in operating system
functions (e.g., kernel 1022, services 1024, and/or drivers 1026),
libraries 1020, and frameworks/middleware 1018 to create user
interfaces to interact with users of the system. Alternatively, or
additionally, in some systems interactions with a user may occur
through a presentation layer, such as the presentation layer 1014.
In these systems, the application/component "logic" can be
separated from the aspects of the application/component that
interact with a user.
Exemplary Machine
[0091] FIG. 11 is a block diagram illustrating components (also
referred to herein as "modules") of a machine 1100, according to
some exemplary embodiments, able to read instructions from a
machine-readable medium (e.g., a machine-readable storage medium)
and perform any one or more of the methodologies discussed herein.
Specifically, FIG. 11 shows a diagrammatic representation of the
machine 1100 in the example form of a computer system, within which
instructions 1110 (e.g., software, a program, an application, an
applet, an app, or other executable code) for causing the machine
1100 to perform any one or more of the methodologies discussed
herein may be executed. As such, the instructions 1110 may be used
to implement modules or components described herein. The
instructions 1110 transform the general, non-programmed machine
1100 into a particular machine 1100 programmed to carry out the
described and illustrated functions in the manner described. In
alternative embodiments, the machine 1100 operates as a standalone
device or may be coupled (e.g., networked) to other machines. In a
networked deployment, the machine 1100 may operate in the capacity
of a server machine or a client machine in a server-client network
environment, or as a peer machine in a peer-to-peer (or
distributed) network environment. The machine 1100 may comprise,
but not be limited to, a server computer, a client computer, a
personal computer (PC), a tablet computer, a laptop computer, a
netbook, a set-top box (STB), a personal digital assistant (PDA),
an entertainment media system, a cellular telephone, a smart phone,
a mobile device, a wearable device (e.g., a smart watch), a smart
home device (e.g., a smart appliance), other smart devices, a web
appliance, a network router, a network switch, a network bridge, or
any machine capable of executing the instructions 1110,
sequentially or otherwise, that specify actions to be taken by
machine 1100. Further, while only a single machine 1100 is
illustrated, the term "machine" shall also be taken to include a
collection of machines that individually or jointly execute the
instructions 1110 to perform any one or more of the methodologies
discussed herein.
[0092] The machine 1100 may include processors 1104, memory/storage
1106, and I/O components 1118, which may be configured to
communicate with each other such as via a bus 1102. The
memory/storage 1106 may include a memory 1114, such as a main
memory, or other memory storage, and a storage unit 1116, both
accessible to the processors 1104 such as via the bus 1102. The
storage unit 1116 and memory 1114 store the instructions 1110
embodying any one or more of the methodologies or functions
described herein. The instructions 1110 may also reside, completely
or partially, within the memory 1114, within the storage unit 1116,
within at least one of the processors 1104 (e.g., within the
processor's cache memory), or any suitable combination thereof,
during execution thereof by the machine 1100. Accordingly, the
memory 1114, the storage unit 1116, and the memory of the
processors 1104 are examples of machine-readable media.
[0093] As used herein, the term "machine-readable medium,"
"computer-readable medium," or the like may refer to any component,
device, or other tangible medium able to store instructions and
data temporarily or permanently. Examples of such media may
include, but are not limited to, random-access memory (RAM),
read-only memory (ROM), buffer memory, flash memory, optical media,
magnetic media, cache memory, other types of storage (e.g.,
Electrically Erasable Programmable Read-Only Memory (EEPROM)),
and/or any suitable combination thereof. The term "machine-readable
medium" should be taken to include a single medium or multiple
media (e.g., a centralized or distributed database, or associated
caches and servers) able to store instructions. The term
"machine-readable medium" may also be taken to include any medium,
or combination of multiple media, that is capable of storing
instructions (e.g., code) for execution by a machine, such that the
instructions, when executed by one or more processors of the
machine, cause the machine to perform any one or more of the
methodologies described herein. Accordingly, a "machine-readable
medium" may refer to a single storage apparatus or device, as well
as "cloud-based" storage systems or storage networks that include
multiple storage apparatus or devices. The term "machine-readable
medium" excludes signals per se.
[0094] The I/O components 1118 may include a wide variety of
components to provide a user interface for receiving input,
providing output, producing output, transmitting information,
exchanging information, capturing measurements, and so on. The
specific I/O components 1118 that are included in the user
interface of a particular machine 1100 will depend on the type of
machine. For example, portable machines such as mobile phones will
likely include a touch input device or other such input mechanisms,
while a headless server machine will likely not include such a
touch input device. It will be appreciated that the I/O components
1118 may include many other components that are not shown in FIG.
11. The I/O components 1118 are grouped according to functionality
merely for simplifying the following discussion and the grouping is
in no way limiting. In various exemplary embodiments, the I/O
components 1118 may include output components 1126 and input
components 1128. The output components 1126 may include visual
components (e.g., a display such as a plasma display panel (PDP), a
light emitting diode (LED) display, a liquid crystal display (LCD),
a projector, or a cathode ray tube (CRT)), acoustic components
(e.g., speakers), haptic components (e.g., a vibratory motor,
resistance mechanisms), other signal generators, and so forth. The
input components 1128 may include alphanumeric input components
(e.g., a keyboard, a touch screen configured to receive
alphanumeric input, a photo-optical keyboard, or other alphanumeric
input components), point-based input components (e.g., a mouse, a
touchpad, a trackball, a joystick, a motion sensor, or other
pointing instruments), tactile input components (e.g., a physical
button, a touch screen that provides location and/or force of
touches or touch gestures, or other tactile input components),
audio input components (e.g., a microphone), and the like. The
input components 1128 may also include one or more image-capturing
devices, such as a digital camera for generating digital images
and/or video.
[0095] In further exemplary embodiments, the I/O components 1118
may include biometric components 1130, motion components 1134,
environment components 1136, or position components 1138, as well
as a wide array of other components. For example, the biometric
components 1130 may include components to detect expressions (e.g.,
hand expressions, facial expressions, vocal expressions, body
gestures, or eye tracking), measure biosignals (e.g., blood
pressure, heart rate, body temperature, perspiration, or brain
waves), identify a person (e.g., voice identification, retinal
identification, facial identification, fingerprint identification,
or electroencephalogram-based identification), and the like. The
motion components 1134 may include acceleration sensor components
(e.g., accelerometer), gravitation sensor components, rotation
sensor components (e.g., gyroscope), and so forth. The environment
components 1136 may include, for example, illumination sensor
components (e.g., photometer), temperature sensor components (e.g.,
one or more thermometers that detect ambient temperature), humidity
sensor components, pressure sensor components (e.g., barometer),
acoustic sensor components (e.g., one or more microphones that
detect background noise), proximity sensor components (e.g.,
infrared sensors that detect nearby objects), gas sensors (e.g.,
gas detection sensors to detect concentrations of hazardous gases
for safety or to measure pollutants in the atmosphere), or other
components that may provide indications, measurements, or signals
corresponding to a surrounding physical environment. The position
components 1138 may include location sensor components (e.g., a GPS
receiver component), altitude sensor components (e.g., altimeters
or barometers that detect air pressure from which altitude may be
derived), orientation sensor components (e.g., magnetometers), and
the like.
[0096] Communication may be implemented using a wide variety of
technologies. The I/O components 1118 may include communication
components 1140 operable to couple the machine 1100 to a network
1132 or devices 1120 via a coupling 1124 and a coupling 1122
respectively. For example, the communication components 1140 may
include a network interface component or other suitable device to
interface with the network 1132. In further examples, the
communication components 1140 may include wired communication
components, wireless communication components, cellular
communication components, Near Field Communication (NFC)
components, Bluetooth.RTM. components (e.g., Bluetooth.RTM. Low
Energy), Wi-Fi.RTM. components, and other communication components
to provide communication via other modalities. The devices 1120 may
be another machine or any of a wide variety of peripheral devices
(e.g., a peripheral device coupled via a USB).
[0097] Moreover, the communication components 1140 may detect
identifiers or include components operable to detect identifiers.
For example, the communication components 1140 may include Radio
Frequency Identification (RFID) tag reader components, NFC smart
tag detection components, optical reader components (e.g., an
optical sensor to detect one-dimensional bar codes such as
Universal Product Code (UPC) bar code, multi-dimensional bar codes
such as Quick Response (QR) code, Aztec code, Data Matrix,
Dataglyph, MaxiCode, PDF4111, Ultra Code, UCC RSS-2D bar code, and
other optical codes), or acoustic detection components (e.g.,
microphones to identify tagged audio signals). In addition, a
variety of information may be derived via the communication
components 1140, such as location via Internet Protocol (IP)
geo-location, location via Wi-Fi.RTM. signal triangulation,
location via detecting an NFC beacon signal that may indicate a
particular location, and so forth.
[0098] Where a phrase similar to "at least one of A, B, or C," "at
least one of A, B, and C," "one or more of A, B, or C," or "one or
more of A, B, and C" is used, it is intended that the phrase be
interpreted to mean that A alone may be present in an embodiment, B
alone may be present in an embodiment, C alone may be present in an
embodiment, or any combination of the elements A, B, and C may be
present in a single embodiment; for example, A and B, A and C, B
and C, or A and B and C may be present.
[0099] Changes and modifications may be made to the disclosed
embodiments without departing from the scope of the present
disclosure. These and other changes or modifications are intended
to be included within the scope of the present disclosure, as
expressed in the following claims.
[0100] A portion of the disclosure of this patent document contains
material that is subject to copyright protection. The copyright
owner has no objection to the facsimile reproduction by anyone of
the patent document or the patent disclosure, as it appears in the
Patent and Trademark Office patent files or records, but otherwise
reserves all copyright rights whatsoever. The following notice
applies to the software and data as described below and in the
drawings that form a part of this document: Copyright 2017,
SNAPCHAT, INC., All Rights Reserved.
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